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Who said it first – Congress or the press?

Sometimes Congress, sometimes the press, it turns out. Matt Shapiro and I wrote a paper for this month’s Midwest Political Science Association meeting in which we analyzed the timing of tweets with hashtags and New York Times articles with keywords and found

… news coverage and Twitter activity from the previous day are good predictors of news coverage and Twitter attention on any given day.

We wondered whether political issues popular on Twitter were popular in the press as well and whether issues cropped up among politicians on Twitter or in the press first. So, we retrieved all the articles available from the New York Times Article API for 2013 and all of the tweets Twitter would let us have for members of Congress (see links to code for collecting data below). We focused on hashtags and article keywords for six policy areas: budget, immigration, environment, energy, the Affordable Care Act (ACA), and marginalized groups (e.g., LGBT, military veterans, Latinos, etc.) and compared the timelines of when those issues were referenced in tweets and in articles.

The tables below show the results of our regressions. For most of the issues, they were similarly popular in the press and on Twitter on the same day. However, for immigration, Twitter activity in the past is a better predictor of news coverage than prior news coverage. For marginalized groups, neither prior news nor prior tweets are good predictors of a day’s news, suggesting that attention both in the news and on Twitter is spotty (or bursty) for marginalized groups.

The strong correlations between issues’ Twitter activity and news coverage on the same day (see models labeled “b” in the tables below) suggest, at least, that the press and Congress are giving attention to similar issues.

  Budget Immigration Environment
  (1a) (1b) (2a) (2b) (3a) (3b)
Previous day’s news .377*** .361*** .202*** .176*** .169*** .169***
Previous day’s tweets .366*** .114* .287*** .224*** .111** .115**
Same day’s tweets .351*** .184*** -.012
F-statistic 145.03 119.71 34.29 27.75 7.97 5.31
R2 0.45 0.50 0.16 0.19 0.04 0.04
N 364 364 364 364 364 364


  Energy ACA Marginalized
  (4a) (4b) (5a) (5b) (6a) (6b)
Previous day’s news .171*** .171*** .382*** .296*** .074 .078
Previous day’s tweets .129** .115** .201*** .073 .007 .035
Same day’s tweets .042 .313*** -.081
F-statistic 9.45 6.50 64.02 57.87 1.00 1.39
R2 0.05 0.05 0.26 0.33 0.01 0.01
N 364 364 364 364 364 364

Note: Each count of articles and tweets is a standard score, and beta coefficients for each predictor are reported. Predictors’ significance are indicated with asterisk where *, **, *** represent p<0.1, p<0.05, p<0.001, respectively.

Python Code for Collecting the Data

So, what do you think ?

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